Towards a sharp estimation of transfer entropy for identifying causality in financial time series

Àlex Serès, Alejandra Cabaña, Argimiro Arratia

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Resum

We present an improvement of an estimator of causality in financial time series via transfer entropy, which includes the side information that may affect the cause-effect relation in the system, i.e. a conditional information-transfer based causality. We show that for weakly stationary time series the conditional transfer entropy measure is nonnegative and bounded below by the Geweke's measure of Granger causality. We use k-nearest neighbor distances to estimate entropy and approximate the distribution of the estimator with bootstrap techniques. We give examples of the application of the estimator in detecting causal effects in a simulated autoregressive stationary system in three random variables with linear and non-linear couplings; in a system of non stationary variables; and with real financial data.

Idioma originalAnglès nord-americà
Pàgines (de-a)31-42
Nombre de pàgines12
RevistaCEUR Workshop Proceedings
Volum1774
Estat de la publicacióPublicada - 2016

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